Edge-Cloud Mobility Task Distribution and Compensation
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Solution Overview
Problem
Current mobility services lack an efficient mechanism to utilize the resources of connected vehicles for executing various applications while ensuring fair compensation to both users and providers, leading to underutilization of vehicle resources and increased operational costs for cloud servers.
Innovation Solution
A mobility IoT system that includes edge devices mounted on vehicles, which execute request applications distributed by a cloud server, with the server generating compensation information based on resource usage, allowing vehicles to be utilized for processing tasks and enabling billing for both users and owners, thereby optimizing resource allocation and reducing cloud server load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud servers execute all processing tasks, then service reliability is maintained, but operational costs increase and server load increases
Solution Approach 1:
The patent segments processing tasks between cloud servers and edge devices. Cloud servers handle task management, distribution, and compensation coordination, while edge devices execute actual processing tasks locally. This segmentation reduces cloud server load and operational costs while maintaining service reliability through distributed execution.
Solution Approach 2:
The patent introduces a compensation management system as an intermediary that coordinates between users, edge device owners, and cloud servers. This intermediary mechanism enables fair compensation for resource usage, incentivizing edge device participation and reducing cloud operational costs without compromising service reliability.
2Reliability
If cloud servers execute all processing tasks, then service quality is maintained, but cloud server load increases
Solution Approach 1:
The patent divides processing workload between cloud servers and edge devices. Cloud servers focus on task management and coordination with reduced processing load, while edge devices handle computationally intensive tasks locally. This segmentation maintains service quality through coordinated execution while significantly reducing cloud server load.
Solution Approach 2:
The patent extends the processing architecture from a single cloud dimension to a distributed cloud-edge dimension. By adding edge devices as another execution dimension, the system reduces cloud server load while maintaining service quality through multi-dimensional task distribution and coordination.
3Productivity
If vehicle resources are utilized for processing tasks, then resource utilization improves, but compensation management complexity increases
Solution Approach 1:
The patent creates a universal compensation management system that handles multiple functions: task distribution, resource usage monitoring, compensation calculation, and payment coordination. This multi-functional approach simplifies what would otherwise be separate complex systems, enabling effective vehicle resource utilization with manageable compensation overhead.
Solution Approach 2:
The patent implements feedback mechanisms where edge devices report resource usage status to the compensation management system, which then adjusts compensation accordingly. This feedback loop automates compensation management, reducing complexity while maintaining fair resource utilization incentives across the distributed system.
4Loss of energy
If edge devices execute tasks locally, then cloud operational costs reduce, but resource allocation efficiency may decrease
Solution Approach 1:
The patent implements preliminary task distribution and resource allocation by the cloud server before edge device execution. This preliminary coordination ensures optimal resource allocation across the distributed system, maintaining efficiency while enabling local execution that reduces cloud operational costs.
Solution Approach 2:
The patent uses feedback mechanisms where edge devices report execution status and resource usage to the cloud server, which then optimizes future task allocation. This feedback-driven allocation maintains resource allocation efficiency while enabling cost-effective local execution at edge devices.
Data Source
AI summary
A cloud server distributes a request application to an edge-equipped vehicle. The edge device executes the request application using the resource of the edge-equipped vehicle, and notifies the cloud server of resource information regarding the resource usage status related to the execution of the request application. The cloud server generates first compensation information to be charged to the user who requests the request application and second compensation information to be paid to the owner of the edge-equipped vehicle according to the resource information.